Innovations

Intelligent Internet of Things for Future Learning Ecosystem

The delivery of a lesson is currently usually a one-size-fits all approach since a huge effort needs to be performed by the lecturer to get information about their students to tailor a lesson plan suited to their needs. Although learning activities and assessment are the common cycle of teaching and learning, scarce mechanisms are available for real time monitoring, learning and making use of the learning dynamics of both the students and the instructor.

Lesson delivery is confined to the comfort-based choice of the instructor with limited insights on the students evolving needs. Some lecturers claimed that they do not have access to student’s data (e.g., contextual, performance, preferences); especially at real time. Besides, the teaching method and classroom environment that attribute to the students performance have never been taken into account. Existing computational system has not been designed to meet these requirements, whilst in this revolutionary era for education, digital-based recommendation to assist lecturers are on demand. This limitation has resulted in unoptimized learning engagement, satisfaction and performance. There is also a need to match the students’ expectation of engaging learning.

Therefore, this research focuses on three key technologies (IOT, learning & educational analytics and AI) as the pillars of the Future Learning Ecosystem. This technological ecosystem is fundamental for supporting flexible curriculum which allows personalise, organic and self-constructed learning: (a) Internet of Things (IOT) allows the immersive and ubiquitous learning to capture the learning activities; (b) AI empowers analytics modeling of the learning behavior to optimise personalised instruction, and maximum learning effectiveness and satisfaction; and (c) through AI based chatbot development, timely service to entertain information seeking customer can be conducted. In this research the solution to the above problems are answered by developing artificial intelligence based solution for IOT-based learning system called Intelligent simulator for Personalised learning (ISPerL).

The effectiveness investigation of ISPerL will undergo primary and secondary data collection arranging from past records in the learning system, development of an array of solutions and user responses. The collected data will be analysed using various quantitative and qualitative analysis techniques. Results will support the relevance of the ISPerL in solving the identified problems.

This project seeks to address the following questions:

1. What are the technological design specifications that could capture learning activities, modeling learning behaviors for optimise personalised instruction, and maximum learning effectiveness and satisfaction, and information seeking support?

2. What are the relationships between learning behavior, achievement and satisfaction?

This project aims to:

a) design and develop an Intelligent simulator for Personalised learning (ISPerL) which is an AI based lesson planner to support an effective delivery of a lesson by predicting activities that could result in high satisfaction by students.

b) evaluate the effectiveness of ISPerL with various quantitative and qualitative experimental methods.